Currently, companies, such as banks and credit card companies, attempt to recognize “suspicious” events or transactions using behavioral analysis. That is, companies attempt to recognize suspicious events or transactions by analyzing the behavior of the customer based on various criteria. Often, these criteria involve multiple transactions over some period of time, involving the same transaction sender or receiver. For instance, if a customer typically performs credit card transactions in the Boston area and then purchases an item to be shipped to Kenya, such a transaction may be deemed to be “suspicious” since it is an anomaly from prior behavior (e.g., transactions limited to the Boston area). As a result, the credit card company may deny such a transaction and temporarily deactivate the credit card account preventing it from being further used.
However, if the customer was planning a safari trip to Kenya and the transaction was a valid transaction, then the customer may be inconvenienced in having to contact the credit card company informing them that it was a valid transaction as the customer will be traveling to Kenya.
Such information about the customer (e.g., the customer planning a safari trip to Kenya) may have been obtained from social media sources. However, since current behavioral analysis relies solely on non-social media sources, “suspicious” events or transactions may be incorrectly identified or not even identified at all.